Network Digital Twin for Congestion-Aware Predictive Traffic Routing using Graph MPNNs

2026-05-22

AI Summary

This work proposes a predictive traffic scheduling framework based on Network Digital Twin (NDT) to overcome the limitations of traditional routing protocols, which react passively only after performance degradation and struggle to handle congestion caused by dynamic traffic and topological changes. The framework uniquely integrates graph generative models—Erdős–Rényi, Barabási–Albert, and Watts–Strogatz—with Message Passing Neural Networks (MPNNs) to continuously mirror and forecast the physical network state in real time. Leveraging a Policy-Based Routing (PBR) feedback mechanism, the approach enables non-intrusive, globally optimized congestion-aware scheduling. Evaluated under synthetic traffic loads, the method accurately classifies link congestion states, significantly improving network throughput and reducing end-to-end latency.

Abstract

Telecom networks scale with growing users and data-intensive applications, generating heavy traffic that causes congestion, reducing throughput, increasing delay, and raising computational costs. Traditional routing protocols act only after performance degradation, making them unsuitable for dynamic traffic and topological changes. Addressing these challenges requires a routing approach that adapts in real time, scales with network growth, operates without disrupting active services, and provides continuous feedback for congestion-aware traffic optimisation. The Network Digital Twin (NDT) addresses these needs by mirroring global network behaviour using Message Passing Neural Networks (MPNNs) through bidirectional communication with the physical network. To align the NDT with physical network behaviour, synthetic traffic is generated with increasing load across topological structures that incrementally scale as routers are added. These topologies are created by graph-generating models such as Erdos-Renyi, Barabasi-Albert, and Watts-Strogatz, customised with vertex degree limitations. The NDT collects performance metrics from routers and links, and MPNNs classify edges based on local vertex and global network behaviours. Based on these classifications, feedback is sent as Policy-Based Routing (PBR) protocol commands to each router, enabling optimal traffic distribution across links of the physical network.

Problem

• Network Digital Twin
• congestion-aware routing
• traffic optimization
• dynamic traffic
• network scalability

Innovation

• Network Digital Twin
• Message Passing Neural Networks
• Congestion-Aware Routing
• Graph Generative Models
• Policy-Based Routing